Self-service analytics is the goal for organizations that want to empower end users to become data-driven decision makers. The semantic layer has been a key pillar in making this a reality.
The problem with traditional semantic layer solutions is that they required additional data copying from the data warehouse into data marts and cubes. Data engineers, architects, and business intelligence specialists know the limitations all too well.
While the concept of using a semantic layer to query data isn’t new, simplified data architectures like the open data lakehouse have expanded the semantic layer’s potential, enabling faster, more efficient reporting and analytics across user groups.
Join Dremio’s solutions experts and learn
Common challenges with semantic layers and how to overcome them
Where the unified semantic layer fits in the open data lakehouse architecture
How a unified semantic layer simplifies ETL for data engineers without data sprawls